Forecasting Oil Consumption: The Statistical Review of World Energy Meets Machine Learning

TL;DR

This paper introduces a machine learning approach to identify key countries influencing global oil demand and demonstrates improved forecasting accuracy by focusing on these dominant drivers, especially during volatile periods.

Contribution

It develops a novel method combining LASSO and OCMT for selecting dominant countries in oil demand forecasting, enhancing prediction performance over traditional models.

Findings

United States identified as a global dominant driver

Including dominant countries improves forecast accuracy

Method performs well during periods of high volatility

Abstract

This paper studies whether a small set of dominant countries can account for most of the dynamics of regional oil demand and improve forecasting performance. We focus on dominant drivers within the OECD and a broad GVAR sample covering over 90\% of world GDP. Our approach identifies dominant drivers from a high-dimensional concentration matrix estimated row by row using two complementary variable-selection methods, LASSO and the one-covariate-at-a-time multiple testing (OCMT) procedure. Dominant countries are selected by ordering the columns of the concentration matrix by their norms and applying a criterion based on consecutive norm ratios, combined with economically motivated restrictions to rule out pseudo-dominance. The United States emerges as a global dominant driver, while France and Japan act as robust regional hubs representing European and Asian components, respectively. Including these dominant drivers as regressors for all countries yields statistically significant forecast gains over autoregressive benchmarks and country-specific LASSO models, particularly during periods of heightened global volatility. The proposed framework is flexible and can be applied to other macroeconomic and energy variables with network structure or spatial dependence.